{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:28:09Z","timestamp":1778200089157,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["22-19-00573"],"award-info":[{"award-number":["22-19-00573"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>For the last two decades, artificial neural networks (ANNs) of the third generation, also known as spiking neural networks (SNN), have remained a subject of interest for researchers. A significant difficulty for the practical application of SNNs is their poor suitability for von Neumann computer architecture, so many researchers are currently focusing on the development of alternative hardware. Nevertheless, today several experimental libraries implementing SNNs for conventional computers are available. In this paper, using the RCNet library, we compare the performance of reservoir computing architectures based on artificial and spiking neural networks. We explicitly show that, despite the higher execution time, SNNs can demonstrate outstanding classification accuracy in the case of complicated datasets, such as data from industrial sensors used for the fault detection of bearings and gears. For one of the test problems, namely, ball bearing diagnosis using an accelerometer, the accuracy of the classification using reservoir SNN almost reached 100%, while the reservoir ANN was able to achieve recognition accuracy up to only 61%. The results of the study clearly demonstrate the superiority and benefits of SNN classificators.<\/jats:p>","DOI":"10.3390\/bdcc7020110","type":"journal-article","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T01:38:26Z","timestamp":1686015506000},"page":"110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Comparing Reservoir Artificial and Spiking Neural Networks in Machine Fault Detection Tasks"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5010-1841","authenticated-orcid":false,"given":"Vladislav","family":"Kholkin","sequence":"first","affiliation":[{"name":"Department of Computer-Aided Design, St. Petersburg Electrotechnical University \u201cLETI\u201d, 5 Professora Popova St., 197022 Saint Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0672-3475","authenticated-orcid":false,"given":"Olga","family":"Druzhina","sequence":"additional","affiliation":[{"name":"Youth Research Institute, St. Petersburg Electrotechnical University \u201cLETI\u201d, 5 Professora Popova St., 197022 Saint Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7679-3014","authenticated-orcid":false,"given":"Valerii","family":"Vatnik","sequence":"additional","affiliation":[{"name":"Department of Computer-Aided Design, St. Petersburg Electrotechnical University \u201cLETI\u201d, 5 Professora Popova St., 197022 Saint Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5075-2292","authenticated-orcid":false,"given":"Maksim","family":"Kulagin","sequence":"additional","affiliation":[{"name":"Youth Research Institute, St. Petersburg Electrotechnical University \u201cLETI\u201d, 5 Professora Popova St., 197022 Saint Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9860-8211","authenticated-orcid":false,"given":"Timur","family":"Karimov","sequence":"additional","affiliation":[{"name":"Youth Research Institute, St. Petersburg Electrotechnical University \u201cLETI\u201d, 5 Professora Popova St., 197022 Saint Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8941-4220","authenticated-orcid":false,"given":"Denis","family":"Butusov","sequence":"additional","affiliation":[{"name":"Department of Computer-Aided Design, St. Petersburg Electrotechnical University \u201cLETI\u201d, 5 Professora Popova St., 197022 Saint Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neunet.2018.12.002","article-title":"Deep learning in spiking neural networks","volume":"111","author":"Tavanaei","year":"2019","journal-title":"Neural Networks"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"651141","DOI":"10.3389\/fnins.2021.651141","article-title":"Comparison of artificial and spiking neural networks on digital hardware","volume":"15","author":"Davidson","year":"2021","journal-title":"Front. Neurosci."},{"key":"ref_3","unstructured":"Przyczyna, D., Pecqueur, S., Vuillaume, D., and Szaci\u0142owski, K. (2020). Reservoir computing for sensing: An experimental approach. arXiv."},{"key":"ref_4","first-page":"310","article-title":"Activation functions in neural networks","volume":"6","author":"Sharma","year":"2017","journal-title":"Towards Data Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neucom.2019.10.104","article-title":"Training multi-layer spiking neural networks using NormAD based spatio-temporal error backpropagation","volume":"380","author":"Anwani","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.neunet.2019.09.005","article-title":"Rethinking the performance comparison between SNNS and ANNS","volume":"121","author":"Deng","year":"2020","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kim, Y., Park, H., Moitra, A., Bhattacharjee, A., Venkatesha, Y., and Panda, P. (2022, January 22\u201327). Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks?. Proceedings of the ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747906"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3304103","article-title":"Spiking neural networks hardware implementations and challenges: A survey","volume":"15","author":"Bouvier","year":"2019","journal-title":"ACM J. Emerg. Technol. Comput. Syst. (JETC)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.neunet.2019.03.005","article-title":"Recent advances in physical reservoir computing: A review","volume":"115","author":"Tanaka","year":"2019","journal-title":"Neural Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.ccr.2018.03.018","article-title":"Molecules, semiconductors, light and information: Towards future sensing and computing paradigms","volume":"365","author":"Pilarczyk","year":"2018","journal-title":"Coord. Chem. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Morando, S., Pera, M.C., Yousfi Steiner, N., Jemei, S., Hissel, D., and Larger, L. (2017, January 11\u201314). Reservoir Computing Optimisation for PEM Fuel Cell Fault Diagnostic. Proceedings of the 2017 IEEE Vehicle Power and Propulsion Conference (VPPC), Belfort, France.","DOI":"10.1109\/VPPC.2017.8330981"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106961","DOI":"10.1016\/j.ymssp.2020.106961","article-title":"Pre-classified reservoir computing for the fault diagnosis of 3D printers","volume":"146","author":"Zhang","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.neunet.2018.03.019","article-title":"Spiking neural networks for handwritten digit recognition\u2014Supervised learning and network optimization","volume":"103","author":"Kulkarni","year":"2018","journal-title":"Neural Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102170","DOI":"10.1016\/j.bspc.2020.102170","article-title":"Energy efficient ECG classification with spiking neural network","volume":"63","author":"Yan","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1109\/LRA.2023.3264836","article-title":"A Hybrid Reinforcement Learning Approach with a Spiking Actor Network for Efficient Robotic Arm Target Reaching","volume":"8","author":"Oikonomou","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108561","DOI":"10.1016\/j.ress.2022.108561","article-title":"A multi-layer spiking neural network-based approach to bearing fault diagnosis","volume":"225","author":"Zuo","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gerstner, W., and Kistler, W.M. (2002). Spiking Neuron Models: Single Neurons, Populations, Plasticity, Cambridge University Press.","DOI":"10.1017\/CBO9780511815706"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1023\/A:1008916026143","article-title":"Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron","volume":"10","author":"Liu","year":"2001","journal-title":"J. Comput. Neurosci."},{"key":"ref_19","unstructured":"Moore, S.C. (2002). Back-Propagation in Spiking Neural Networks. [Master\u2019s Thesis, University of Bath]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.neunet.2020.02.011","article-title":"Supervised learning in spiking neural networks: A review of algorithms and evaluations","volume":"125","author":"Wang","year":"2020","journal-title":"Neural Networks"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.neucom.2012.08.017","article-title":"Tree echo state networks","volume":"101","author":"Gallicchio","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_22","first-page":"111","article-title":"Performance analysis of various activation functions in generalized MLP architectures of neural networks","volume":"1","author":"Karlik","year":"2011","journal-title":"Int. J. Artif. Intell. Expert Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1162\/089976602760407955","article-title":"Real-time computing without stable states: A new framework for neural computation based on perturbations","volume":"14","author":"Maass","year":"2002","journal-title":"Neural Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3637","DOI":"10.1152\/jn.00686.2005","article-title":"Adaptive exponential integrate-and-fire model as an effective description of neuronal activity","volume":"94","author":"Brette","year":"2005","journal-title":"J. Neurophysiol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gerstner, W., Kistler, W.M., Naud, R., and Paninski, L. (2014). Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition, Cambridge University Press.","DOI":"10.1017\/CBO9781107447615"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1080\/07313569208909609","article-title":"Methods of motor current signature analysis","volume":"20","author":"Kliman","year":"1992","journal-title":"Electr. Mach. Power Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1109\/28.475697","article-title":"Motor bearing damage detection using stator current monitoring","volume":"31","author":"Schoen","year":"1995","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1109\/TIA.2010.2049623","article-title":"Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison","volume":"46","author":"Immovilli","year":"2010","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wagner, T., and Sommer, S. (2021, January 27\u201330). Feature Based Bearing Fault Detection With Phase Current Sensor Signals Under Different Operating Conditions. Proceedings of the PHM Society European Conference, Turin, Italy.","DOI":"10.36001\/phme.2021.v6i1.2852"},{"key":"ref_30","unstructured":"(2022, December 10). Case Western Reserve University Bearing Data Center. Available online: https:\/\/engineering.case.edu\/bearingdatacenter\/download-data-file."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/TAFFC.2019.2961089","article-title":"An active learning paradigm for online audio-visual emotion recognition","volume":"13","author":"Kansizoglou","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_32","unstructured":"(2022, December 10). Gearbox Fault Diagnosis: Stacked Datasets. Available online: https:\/\/www.kaggle.com\/datasets\/brjapon\/gearbox-fault-diagnosis-stacked-datasets."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/2\/110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:48:31Z","timestamp":1760125711000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/2\/110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,5]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["bdcc7020110"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7020110","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,5]]}}}